Training 1-Bit Networks on a Sphere: A Geometric Approach

Author:

Guerra Luis,Thalaiyasingam Ajanthan,Avraham Gil,Zou Yan,Drummond Tom

Publisher

Springer Nature Switzerland

Reference53 articles.

1. Ajanthan, T., Dokania, P.K., Hartley, R., Torr, P.H.: Proximal mean-field for neural network quantization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4871–4880 (2019)

2. Anderson, A.G., Berg, C.P.: The high-dimensional geometry of binary neural networks. arXiv preprint arXiv:1705.07199 (2017)

3. Bengio, Y., Léonard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)

4. Bonnabel, S.: Stochastic gradient descent on Riemannian manifolds. IEEE Trans. Autom. Control 58(9), 2217–2229 (2013)

5. Boumal, N., Mishra, B., Absil, P.A., Sepulchre, R.: Manopt, a Matlab toolbox for optimization on manifolds. J. Mach. Learn. Res. 15(42), 1455–1459 (2014). https://www.manopt.org

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